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Transthyretin amyloid cardiomyopathy among patients hospitalized for heart failure and performance of an adapted wild-type ATTR-CM machine learning model: Findings from GWTG-HF.

Publication ,  Conference
Peters, AE; Solomon, N; Chiswell, K; Fonarow, GC; Khouri, MG; Baylor, L; Alvir, J; Bruno, M; Huda, A; Allen, LA; Sharma, K; DeVore, AD; Greene, SJ
Published in: Am Heart J
November 2023

BACKGROUND: An 11-factor random forest model has been developed among ambulatory heart failure (HF) patients for identifying potential wild-type amyloidogenic TTR cardiomyopathy (wtATTR-CM). The model has not been evaluated in a large sample of patients hospitalized for HF. METHODS: This study included Medicare beneficiaries aged ≥65 years hospitalized for HF in the Get With The Guidelines-HF® Registry from 2008-2019. Patients with and without a diagnosis of ATTR-CM were compared, as defined by inpatient and outpatient claims data within 6 months pre- or post-index hospitalization. Within a cohort matched 1:1 by age and sex, univariable logistic regression was used to evaluate relationships between ATTR-CM and each of the 11 factors of the established model. Discrimination and calibration of the 11-factor model were assessed. RESULTS: Among 205,545 patients (median age 81 years) hospitalized for HF across 608 hospitals, 627 patients (0.31%) had a diagnosis code for ATTR-CM. Univariable analysis within the 1:1 matched cohort of each of the 11-factors in the ATTR-CM model found pericardial effusion, carpal tunnel syndrome, lumbar spinal stenosis, and elevated serum enzymes (e.g., troponin elevation) to be strongly associated with ATTR-CM. The 11-factor model showed modest discrimination (c-statistic 0.65) and good calibration within the matched cohort. CONCLUSIONS: Among US patients hospitalized for HF, the number of patients with ATTR-CM defined by diagnosis codes on an inpatient/outpatient claim within 6 months of admission was low. Most factors within the prior 11-factor model were associated with greater odds of ATTR-CM diagnosis. In this population, the ATTR-CM model demonstrated modest discrimination.

Duke Scholars

Published In

Am Heart J

DOI

EISSN

1097-6744

Publication Date

November 2023

Volume

265

Start / End Page

22 / 30

Location

United States

Related Subject Headings

  • Cardiovascular System & Hematology
  • 3201 Cardiovascular medicine and haematology
  • 1117 Public Health and Health Services
  • 1102 Cardiorespiratory Medicine and Haematology
 

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Peters, A. E., Solomon, N., Chiswell, K., Fonarow, G. C., Khouri, M. G., Baylor, L., … Greene, S. J. (2023). Transthyretin amyloid cardiomyopathy among patients hospitalized for heart failure and performance of an adapted wild-type ATTR-CM machine learning model: Findings from GWTG-HF. In Am Heart J (Vol. 265, pp. 22–30). United States. https://doi.org/10.1016/j.ahj.2023.06.013
Peters, Anthony E., Nicole Solomon, Karen Chiswell, Gregg C. Fonarow, Michel G. Khouri, Lori Baylor, Jose Alvir, et al. “Transthyretin amyloid cardiomyopathy among patients hospitalized for heart failure and performance of an adapted wild-type ATTR-CM machine learning model: Findings from GWTG-HF.” In Am Heart J, 265:22–30, 2023. https://doi.org/10.1016/j.ahj.2023.06.013.
Peters AE, Solomon N, Chiswell K, Fonarow GC, Khouri MG, Baylor L, Alvir J, Bruno M, Huda A, Allen LA, Sharma K, DeVore AD, Greene SJ. Transthyretin amyloid cardiomyopathy among patients hospitalized for heart failure and performance of an adapted wild-type ATTR-CM machine learning model: Findings from GWTG-HF. Am Heart J. 2023. p. 22–30.
Journal cover image

Published In

Am Heart J

DOI

EISSN

1097-6744

Publication Date

November 2023

Volume

265

Start / End Page

22 / 30

Location

United States

Related Subject Headings

  • Cardiovascular System & Hematology
  • 3201 Cardiovascular medicine and haematology
  • 1117 Public Health and Health Services
  • 1102 Cardiorespiratory Medicine and Haematology